no code implementations • 19 May 2024 • Sanchit Sinha, Yuguang Yue, Victor Soto, Mayank Kulkarni, Jianhua Lu, Aidong Zhang
In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only perform well on disjointed tasks but also adapts to unseen tasks.
no code implementations • 1 May 2024 • Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
With the wide proliferation of Deep Neural Networks in high-stake applications, there is a growing demand for explainability behind their decision-making process.
no code implementations • 2 Jul 2023 • Anshu Bhatia, Sanchit Sinha, Saket Dingliwal, Karthik Gopalakrishnan, Sravan Bodapati, Katrin Kirchhoff
Speech representations learned in a self-supervised fashion from massive unlabeled speech corpora have been adapted successfully toward several downstream tasks.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 5 Jun 2023 • Jianhui Sun, Sanchit Sinha, Aidong Zhang
We approximate the dynamic of PGD-AT by a continuous-time Stochastic Differential Equation (SDE), and show that the diffusion term of this SDE determines the robust generalization.
no code implementations • 29 Nov 2022 • Sanchit Sinha, Mengdi Huai, Jianhui Sun, Aidong Zhang
Subsequently, we propose a potential general adversarial training-based defense mechanism to increase robustness of these systems to the proposed malicious attacks.
1 code implementation • EMNLP (BlackboxNLP) 2021 • Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon, Yangfeng Ji, Yanjun Qi
Via a small portion of word-level swaps, these adversarial perturbations aim to make the resulting text semantically and spatially similar to its seed input (therefore sharing similar interpretations).